CN114170427A - Wireless microwave rain attenuation model SSIM image similarity evaluation method based on rain cells - Google Patents
Wireless microwave rain attenuation model SSIM image similarity evaluation method based on rain cells Download PDFInfo
- Publication number
- CN114170427A CN114170427A CN202111340003.6A CN202111340003A CN114170427A CN 114170427 A CN114170427 A CN 114170427A CN 202111340003 A CN202111340003 A CN 202111340003A CN 114170427 A CN114170427 A CN 114170427A
- Authority
- CN
- China
- Prior art keywords
- rainfall
- image
- ssim
- data
- rain
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
Abstract
The invention discloses a wireless microwave rain attenuation model SSIM image similarity evaluation method based on a rain cell, which comprises the following steps: s10, obtaining the attenuation data of the wireless microwave affected by the weather from the signal receiver, and preprocessing the data; s20, constructing a link rainfall rate distribution formula based on a Gaussian raincell concept, and establishing a rain attenuation model containing parameters to be optimized for the integral; s30, obtaining corresponding parameters as a final result by using the existing data and a genetic algorithm; s40, respectively drawing a rainfall map of a fixed certain period of time in a corresponding area in a mode that the image block brightness and rainfall form positive correlation coefficients by using the wireless microwave data and the hydrological station data processed by the model; and S50, evaluating the similarity of the images based on the structural similarity SSIM. The invention provides an image similarity analysis means taking the brightness of image blocks as a core, and establishes an evaluation standard which can well evaluate the objective similarity of images and can be consistent with the subjective feeling of human eyes.
Description
Technical Field
The invention relates to a wireless microwave rain attenuation model SSIM image similarity evaluation method based on rain cells, and belongs to the technical field of communication.
Background
The production and life of the modern society have urgent requirements on the high-precision forecast of rainfall. The rainfall station taking the 'point' data as the core is difficult to carry out high-precision rainfall forecast on non-monitored areas. The emergence of wireless microwave technology also provides the possibility of high-precision rainfall forecast in non-monitored areas. This involves the calculation of rain attenuation, which is very simple under conditions of uniform rainfall. But the non-uniformity of the rain medium is random, which greatly complicates the accurate calculation of rain attenuation. The traditional modeling method has two ideas based on an equivalent path length method and a path average rainfall rate method, however, the two modeling methods are both described by adopting an equivalent simple method for rainfall heterogeneity, and although the calculation process is simple, the calculation process has no clear physical significance, and basically can be calculated as an empirical mode. Although the rain attenuation model established on the basis of the columnar rain cells is also described on the spatial distribution of rainfall, the rain attenuation model is actually a rough spatial distribution with a constant value of rainfall rate, and the rainfall rate distribution is not in accordance with the actual situation. Meanwhile, for the precision test of the rain attenuation model, internationally, the logarithm of the ratio of the attenuation calculation value to the measurement value is generally used as a test variable, and the comparison is carried out through other rain attenuation modes. Therefore, the traditional rain attenuation model and the evaluation method thereof have certain limitations.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a wireless microwave rain attenuation model SSIM image similarity evaluation method based on rain cells, provides an image similarity analysis means taking block brightness as a core, and establishes an evaluation standard which can evaluate the objective similarity of images well and can be consistent with the subjective feeling of human eyes.
The technical scheme is as follows: in order to solve the technical problems, the wireless microwave rain attenuation model SSIM image similarity evaluation method based on the rain cells comprises the following steps:
s10, obtaining the attenuation data of the wireless microwave affected by the weather from the signal receiver, and preprocessing the data;
s20, constructing a link rainfall rate distribution formula based on a Gaussian raincell concept, and establishing a rain attenuation model containing parameters to be optimized for the integral;
s30, using the existing data and adopting the genetic algorithm to optimize and regress the parameters in the rain attenuation model formula, and obtaining the corresponding parameters when the root mean square error is minimum as the final result;
s40, respectively drawing a rainfall map of a fixed certain period of time in a corresponding area in a mode that the image block brightness and rainfall form positive correlation coefficients by using the wireless microwave data and the hydrological station data processed by the model;
and S50, obtaining a brightness comparison function, a contrast comparison function and a structure comparison function of the two images, and evaluating the similarity of the images based on the structure similarity SSIM.
Preferably, the preprocessing in step S10 is: according to data of a hydrological station or a meteorological station, screening attenuation data only affected by rainfall, removing interference generated by non-rainfall influence factors, performing mean interpolation on lost data, and removing abnormal data.
Preferably, in step S20, assuming that there is a gaussian distribution of raincells in the propagation path, the distribution r (x) of the rainfall rate in the path includes:
r (0) is more than or equal to 5mm/h, wherein x is the distance from the observation station, and the maximum rainfall on the link occurs when x is LDAnd b is a raincell diameter parameter to be optimized, and f is a raincell amplification parameter to be optimized.
Preferably, in step S20, the total attenuation formula on the whole propagation path includes:
wherein, L is the propagation path length of the microwave, k, alpha are parameters related to the microwave frequency, the polarization angle and the like, alpha is the attenuation coefficient, k is the attenuation index, and the ITU-R recommendation can be found.
Preferably, the main steps of performing genetic algorithm regression and optimization on b and f in the total attenuation formula to obtain the final parameters comprise:
(1) binary coding is carried out on the variables in the variable change range;
(2) generating n individuals as an initial genetic algorithm population D (T), setting the initial state T to be 0, setting the maximum genetic evolution algebra T, taking n as 20-100, and taking T as 300-;
(3) calculating the fitness of each individual in the calculation group D (0), wherein the fitness function is the minimum value of the root mean square value of the relative percentage error;
(4) selecting 2n individuals in D (0) according to a probability standard in inverse proportion to a fitness function, and selecting two groups of n individuals as a next hybridization operation sample to be a parent sample;
(5) and (5) performing hybridization operation. Performing pairwise hybridization pairing on the two selected parent individuals according to the hybridization probability Pi to generate two groups of filial individuals, wherein Pi is 0.3-0.6;
(6) and (5) performing mutation operation, namely randomly selecting one of the two groups of descendants obtained by calculation in the calculation step (5), and performing intra-group mutation according to the mutation probability Pv. The initial population D (0) is subjected to hybridization operation and mutation operation to obtain a next generation population D (1), and Pv is 0.03-0.1.
(7) Substituting the new population D (1) obtained in the step (6) as a new parent population into the step (3), carrying out next evolutionary evolution, and carrying out fitness calculation, selection, hybridization operation and mutation operation again, wherein the steps are repeated twice;
(8) the search is accelerated. Taking the variation ranges of the m excellent individuals generated by the first evolution iteration and the second evolution iteration as the latest value ranges of the variables, and performing the calculation step (1) again; repeating the above calculation steps will gradually reduce the variation interval of the good individual, the distance from the optimal point will be closer and closer, the iteration will continue until the function value of the good individual is less than a certain set value or reaches the expected iteration number T, at this time, the output best individual value is the best solution.
Preferably, the step S40 includes the steps of:
(3) measuring the total attenuation value of the link, observing by radar to obtain the coordinate of the maximum rainfall point in the propagation path, and calculating the distance L between the observation station and the linkDCollecting rainfall time, rainfall duration, rainfall amount and wind direction information in the decay time, classifying the information according to the prior grouping information, and obtaining b and f values under the rainfall condition from the prior information;
(4) obtaining rainfall rate distribution R (x) in the propagation path according to the existing b and f values;
(3) uniformly taking points in each propagation path, calculating the rainfall rate of the relevant coordinate, integrating the coordinate positions selected by all links and the rainfall, and drawing a rainfall map of a fixed certain period of time in the corresponding area in a mode that the image block brightness and the rainfall rate form a positive correlation coefficient.
Preferably, the process of evaluating the similarity of the image based on the structural similarity SSIM includes:
(1) setting a rainfall map drawn by data measured by a hydrological station as a reference image, and setting an image drawn by data obtained after wireless microwave data is calculated by a rain attenuation model as a comparison image;
(2) calculating a brightness mean value, a brightness standard deviation and a brightness covariance;
let x and y be reference image and contrast image block, reference respectivelyMean value of the brightness μ of image xxThe formula is as follows:
wherein x isiThe ith pixel value of the reference image x is represented, N represents the number of pixels, and the brightness mean value mu of the contrast image y can be obtained by the same methody；
With the help of the luminance mean formula of the image, the luminance standard deviation formula of the image x can be obtained:
the brightness standard deviation sigma of the contrast image y can be obtained in the same wayy；
σxyThe structural correlation of the images can be reflected by the covariance of the brightness of the reference image x and the contrast image y, and the formula is:
(3) calculating a brightness comparison function, a contrast comparison function and a structure comparison function:
the brightness comparison function is:
the contrast comparison function is:
the structure degree comparison function is:
wherein, C1、C2、C3Is a small constant set to avoid denominator being zero;
(4) calculating the SSIM value: the formula for SSIM is: SSIM (x, y) ═ l (x, y)]α[c(x,y)]β[s(x,y)]γWherein, alpha, beta and gamma are parameters forAdjusting the proportion of 3 comparison functions, taking alpha as beta as gamma as 1, and making C3＝C2And/2, the above formula can be simplified as follows:
wherein, considering the value range of image brightness and the influence on SSIM value, let C1＝C2The SSIM value indicates the degree of similarity between two images, that is, 0.0001.
Preferably, when the SSIM value is 0.6 or more, the reference image and the comparison image satisfy the similarity determination criterion. And when the SSIM value is larger than or smaller than 0.6, regressing the values of b and f by using the genetic algorithm again until the condition is met, and if proper values of b and f cannot be found, judging that the rain attenuation model is not suitable for the rainfall in the current area.
Has the advantages that: the wireless microwave rain attenuation model SSIM image similarity evaluation method based on the rain cells provides an image similarity analysis means taking the brightness of the image blocks as a core, and establishes an evaluation standard which can evaluate the objective similarity of the image well and can be consistent with the subjective feeling of human eyes.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
As shown in fig. 1, a wireless microwave rain attenuation model SSIM image similarity evaluation method based on rain cells includes the following steps:
s10, obtaining attenuation data of the wireless microwave affected by weather from the signal receiver, and preprocessing the attenuation data;
after the microwave attenuation signal intensity data of the microwave signal receiving end is obtained, the microwave attenuation signal intensity data is preliminarily processed, attenuation data only affected by rainfall is screened out according to data of a hydrological station or a meteorological station, interference caused by factors of non-rainfall influence such as strong wind, sand dust and hail is removed, mean value interpolation is carried out on lost data, and abnormal data are manually removed; and simultaneously grouping the data of the hydrological station and the meteorological station according to the similar rainfall time, duration, rainfall amount and wind direction.
S20, constructing a link rainfall rate distribution formula based on a Gaussian raincell concept, and establishing a rain attenuation model containing parameters to be optimized for the integral;
s30, using the existing data, using the genetic algorithm to optimize and regress the parameters in the model, and obtaining the corresponding parameters when the root mean square error is minimum as the final result;
s40, respectively drawing a rainfall map of a fixed certain period of time in a corresponding area in a mode that the brightness of an image block and the rainfall form a positive correlation coefficient by utilizing the wireless microwave data and the hydrological station data;
the wireless microwave rainfall data obtained after the wireless microwave rainfall data is calculated through the rain attenuation model is used for representing the spatial distribution of rainfall in the form of images, existing hydrological station monitoring data are integrated, rainfall maps of corresponding areas in a fixed period are respectively drawn in a mode that image block brightness and rainfall form positive correlation coefficients after a certain fixed area and a certain fixed period are determined, the rainfall maps drawn through the data measured by the hydrological station are set as reference images, and images drawn through the data obtained after the wireless microwave data is calculated through the rain attenuation model are set as comparison images, so that SSIM similarity evaluation is conducted.
And S50, obtaining a brightness comparison function, a contrast comparison function and a structure comparison function of the two images, and evaluating the similarity of the images based on the structure similarity SSIM.
The brightness comparison function, the contrast comparison function and the structure comparison function of the two images are obtained in the steps, the proportion of 3 evaluation factors is taken as 1, similarity evaluation is carried out on the images based on the structure similarity SSIM, and a rainfall spatial distribution evaluation standard which can evaluate the objective similarity of the images well and can be consistent with the subjective feeling of human eyes can be established.
The wireless microwave rain attenuation model SSIM image similarity evaluation method based on the rain cells comprises the steps of obtaining attenuation data of wireless microwaves affected by weather from a signal receiver, preprocessing the data, simulating spatial distribution of rainfall according to Gaussian rain cell distribution, establishing a rain attenuation model containing parameters to be optimized, optimizing and regressing the parameters in the model by using the existing data and adopting a genetic algorithm to obtain a final result of the corresponding parameters when the root mean square error is minimum, respectively drawing fixed rainfall maps of corresponding areas in a mode that image block brightness and rainfall form positive correlation coefficients by using the wireless microwave data and the data of a hydrological station processed by the model, setting the rainfall maps drawn by the data measured by the hydrological station as reference images, and setting images drawn by the data obtained after the wireless microwave data are calculated by the rain attenuation model as comparison images, the method comprises the steps of obtaining a brightness comparison function, a contrast comparison function and a structure comparison function of two images, taking the proportion of 3 evaluation factors as 1, carrying out similarity evaluation on the images based on the structure similarity SSIM, describing the spatial distribution of rainfall more accurately from a physical angle, and establishing a rainfall spatial distribution evaluation standard which can evaluate the objective similarity of the images well and is consistent with the subjective feeling of human eyes.
In one embodiment, the preprocessing the attenuation data includes:
screening attenuation data only affected by rainfall according to data of a hydrological station or a meteorological station, removing interference generated by non-rainfall influence factors such as strong wind, sand dust, hail and the like, performing mean interpolation on lost data, and manually rejecting abnormal data; and simultaneously grouping the data of the hydrological station and the meteorological station according to the similar rainfall time, duration, rainfall amount and wind direction.
In one embodiment, the process of constructing a link rainfall rate distribution formula based on the concept of Gaussian raincells and establishing a rain attenuation model for the integral comprises:
(1) assuming that there is a gaussian distribution of rain cells in the propagation path, the distribution of rainfall rate in the path r (x) includes:
wherein x is the distance from the observation station. The maximum rainfall on the link occurs at x-LDAnd calculating the distance between the observation station and the observation station through the coordinate of the point with the maximum rainfall rate in the radar observation link to obtain LDThe specific value. b is a raincell diameter parameter to be optimized, and f is a raincell amplification parameter to be optimized.
(2) The total attenuation of rain on the microwave propagation path is:
where L is the propagation path length of the microwave, and k, α are parameters related to the microwave frequency, polarization angle, and the like.
(3) The rainfall rate distribution R (x) based on Gaussian raincells is substituted and simplified to obtain a total attenuation formula on the whole propagation path:
(4) and (3) performing genetic algorithm regression and optimization on b and f in the S23 total attenuation formula by using the classified data and combining the measured link total attenuation values to obtain final parameters, so as to obtain link rainfall rate distribution R (x) suitable for the rainfall time, rainfall duration, rainfall and wind direction conditions of the area.
In an experimental example, the main contents of the genetic algorithm regression and optimization on b and f in the total attenuation formula to obtain the final parameters include:
(1) binary coding is carried out on the variable within a variable variation range, and the variable variation range is determined according to specific conditions;
(2) generating n individuals as an initial genetic algorithm population D (T), setting the initial state T to be 0, setting the maximum genetic evolution algebra T, taking n as 20-100, and taking T as 300-;
(3) calculating the fitness of each individual in the calculation group D (0), wherein the fitness function is the minimum value of the root mean square value of the relative percentage error;
(4) selecting 2n individuals in D (0) according to a probability standard in inverse proportion to a fitness function, and selecting two groups of n individuals as a next hybridization operation sample to be a parent sample;
(5) and (5) performing hybridization operation. Performing pairwise hybridization pairing on the two selected parent individuals according to the hybridization probability Pi to generate two groups of filial individuals, wherein Pi is 0.3-0.6;
(6) and (5) performing mutation operation. Randomly selecting one of the two groups of descendants obtained by calculation in the calculation step (5), and carrying out variation in the individual group according to the variation probability Pv. The initial population D (0) is subjected to hybridization operation and mutation operation to obtain a next generation population D (1), and Pv is 0.03-0.1.
(7) And (4) taking the new population D (1) obtained in the step (6) as a new parent population to be substituted into the step (3), carrying out the next evolutionary evolution, and carrying out fitness calculation, selection, hybridization operation and mutation operation again, wherein the steps are repeated twice.
(8) The search is accelerated. Taking the variation ranges of the m excellent individuals generated by the first evolution iteration and the second evolution iteration as the latest value ranges of the variables, and performing the calculation step (1) again; repeating the above calculation steps will gradually reduce the variation interval of the good individual, the distance from the optimal point will be closer and closer, the iteration will continue until the function value of the good individual is less than a certain set value or reaches the expected iteration number T, at this time, the output best individual value is the best solution.
In one embodiment, processing the wireless microwave data with a rain attenuation model to map the content of the rainfall map comprises:
(1) measuring the total attenuation value of the link, observing by radar to obtain the coordinate of the maximum rainfall point in the propagation path, and calculating the distance L between the observation station and the linkDCollecting rainfall time, rainfall duration, rainfall amount and wind direction information in the decay time, classifying the information according to the prior grouping information, and obtaining b and f values under the rainfall condition from the prior information;
(2) obtaining rainfall rate distribution R (x) in the propagation path according to the existing b and f values;
(3) uniformly taking points in each propagation path, calculating the rainfall rate of the relevant coordinate, integrating the coordinate positions selected by all links and the rainfall, and drawing a rainfall map of a fixed certain period of time in the corresponding area in a mode that the image block brightness and the rainfall rate form a positive correlation coefficient.
In one embodiment, obtaining a brightness comparison function, a contrast comparison function and a structural similarity comparison function of two images, and performing a similarity evaluation process on the images based on the structural similarity SSIM includes:
(1) setting a rainfall map drawn by data measured by a hydrological station as a reference image, and setting an image drawn by data obtained after wireless microwave data is calculated by a rain attenuation model as a contrast image
(2) And calculating the brightness mean value, the brightness standard deviation and the brightness covariance.
Let x and y be the reference image and the contrast image block respectively, the mean value mu of the brightness of the reference image xxThe formula is as follows:
wherein x isiThe ith pixel value of the reference image x is represented, N represents the number of pixels, and the brightness mean value mu of the contrast image y can be obtained by the same methody。
With the help of the luminance mean formula of the image, the luminance standard deviation formula of the image x can be obtained:
the brightness standard deviation sigma of the contrast image y can be obtained in the same wayy。
σxyThe structural correlation of the images can be reflected by the covariance of the brightness of the reference image x and the contrast image y, and the formula is:
(3) and calculating a brightness comparison function, a contrast comparison function and a structure comparison function.
The brightness comparison function is:
the contrast comparison function is:
the structure degree comparison function is:
wherein, C1、C2、C3Is a small constant set to avoid the denominator being zero.
(4) The SSIM value is calculated.
The formula for SSIM is:
SSIM(x,y)＝[l(x,y)]α[c(x,y)]β[s(x,y)]γ
wherein, α, β, γ are parameters for adjusting the ratio of the 3 comparison functions, and α ═ β ═ γ ═ 1 is taken.
Let C3＝C2And/2, the above formula can be simplified as follows:
wherein, considering the value range of image brightness and the influence on SSIM value, let C1＝C20.0001. The size of the SSIM value indicates the degree of similarity between two images.
In one embodiment, the similarity evaluation criteria of the reference image and the contrast image include:
when the SSIM value is more than or equal to 0.6, the reference image and the comparison image meet the similarity judgment standard, and the rain attenuation model containing the parameters is considered to be suitable for the rainfall in the current region. If not, the parameters are optimized again until the model parameters meeting the evaluation criteria are obtained.
The wireless microwave rain attenuation model SSIM image similarity evaluation method based on the rain cells is characterized in that a rain attenuation model with physical significance is established by using a rain cell concept, and similarity evaluation is performed on images by using structural similarity SSIM, and has the following advantages:
(1) a rain attenuation model which is based on the Gaussian rain cell concept, has physical significance and can describe the spatial distribution of rainfall is provided.
(2) The method and the device for evaluating the rain attenuation model provide a new idea for evaluating the rain attenuation model by adopting a rainfall image contrast analysis mode.
An image similarity analysis means taking the image block brightness as a core is provided, and an evaluation standard which can well evaluate the objective similarity of the image and can be consistent with the subjective feeling of human eyes is established.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (8)
1. The wireless microwave rain attenuation model SSIM image similarity evaluation method based on the rain cells is characterized by comprising the following steps of:
s10, obtaining the attenuation data of the wireless microwave affected by the weather from the signal receiver, and preprocessing the data;
s20, constructing a link rainfall rate distribution formula based on a Gaussian raincell concept, and establishing a rain attenuation model containing parameters to be optimized for the integral;
s30, using the existing data and adopting the genetic algorithm to optimize and regress the parameters in the rain attenuation model formula, and obtaining the corresponding parameters when the root mean square error is minimum as the final result;
s40, respectively drawing a rainfall map of a fixed certain period of time in a corresponding area in a mode that the image block brightness and rainfall form positive correlation coefficients by using the wireless microwave data and the hydrological station data processed by the model;
and S50, obtaining a brightness comparison function, a contrast comparison function and a structure comparison function of the two images, and evaluating the similarity of the images based on the structure similarity SSIM.
2. The wireless microwave rain attenuation model SSIM image similarity evaluation method based on rain cells according to claim 1, wherein the preprocessing in the step S10 is as follows: according to data of a hydrological station or a meteorological station, screening attenuation data only affected by rainfall, removing interference generated by non-rainfall influence factors, performing mean interpolation on lost data, and removing abnormal data.
3. The method for evaluating similarity of SSIM images according to claim 1, wherein in step S20, assuming that there is a gaussian distribution of rain cells in the propagation path, the distribution of rainfall rate in the path r (x) includes:
r (0) is more than or equal to 5mm/h, wherein x is the distance from the observation station, and the maximum rainfall on the link occurs when x is LDAnd b is a raincell diameter parameter to be optimized, and f is a raincell amplification parameter to be optimized.
4. The method for evaluating similarity of SSIM images according to claim 3, wherein in step S20, the total attenuation formula over the entire propagation path includes:
where L is the propagation path length of the microwave, and k, α are parameters related to the microwave frequency, polarization angle, and the like.
5. The wireless microwave rain attenuation model SSIM image similarity evaluation method based on rain cells as claimed in claim 3, wherein the main steps of performing genetic algorithm regression on b and f in the total attenuation formula, optimizing to obtain final parameters comprise:
(1) binary coding is carried out on the variables in the variable change range;
(2) generating n individuals as an initial genetic algorithm population D (T), setting the initial state T to be 0, setting the maximum genetic evolution algebra T, taking n as 20-100, and taking T as 300-;
(3) calculating the fitness of each individual in the calculation group D (0), wherein the fitness function is the minimum value of the root mean square value of the relative percentage error;
(4) selecting 2n individuals in D (0) according to a probability standard in inverse proportion to a fitness function, and selecting two groups of n individuals as a next hybridization operation sample to be a parent sample;
(5) performing hybridization operation, namely performing pairwise hybridization pairing on the two selected parent individuals according to the hybridization probability Pi to generate two groups of filial generation individuals, wherein Pi is 0.3-0.6;
(6) performing variation operation, namely randomly selecting one of the two groups of filial generations calculated in the calculation step (5), performing individual intra-group variation according to variation probability Pv, obtaining a next generation group D (1) by performing hybridization operation and variation operation on an initial group D (0), and taking Pv as 0.03-0.1;
(7) substituting the new population D (1) obtained in the step (6) as a new parent population into the step (3), carrying out next evolutionary evolution, and carrying out fitness calculation, selection, hybridization operation and mutation operation again, wherein the steps are repeated twice;
(8) accelerating search, namely taking the variation ranges of the m excellent individuals generated by the first and second evolutionary iterations as the latest value ranges of the variable, and performing the calculation step (1) again; repeating the above calculation steps will gradually reduce the variation interval of the good individual, the distance from the optimal point will be closer and closer, the iteration will continue until the function value of the good individual is less than a certain set value or reaches the expected iteration number T, at this time, the output best individual value is the best solution.
6. The method for evaluating similarity of wireless microwave rain attenuation model SSIM according to claim 5, wherein the step S40 comprises the following steps:
(1) measuring the total attenuation value of the link, observing by radar to obtain the coordinate of the maximum rainfall point in the propagation path, and calculating the distance L between the observation station and the linkDCollecting rainfall time, rainfall duration, rainfall amount and wind direction information in the decay time, classifying the information according to the prior grouping information, and obtaining b and f values under the rainfall condition from the prior information;
(2) obtaining rainfall rate distribution R (x) in the propagation path according to the existing b and f values;
(3) uniformly taking points in each propagation path, calculating the rainfall rate of the relevant coordinate, integrating the coordinate positions selected by all links and the rainfall, and drawing a rainfall map of a fixed certain period of time in the corresponding area in a mode that the image block brightness and the rainfall rate form a positive correlation coefficient.
7. The wireless microwave rain attenuation model SSIM image similarity evaluation method based on rain cells as claimed in claim 1, wherein the process of similarity evaluation of the image based on the structural similarity SSIM comprises:
(1) setting a rainfall map drawn by data measured by a hydrological station as a reference image, and setting an image drawn by data obtained after wireless microwave data is calculated by a rain attenuation model as a comparison image;
(2) calculating a brightness mean value, a brightness standard deviation and a brightness covariance;
let x and y be the reference image and the contrast image block respectively, the mean value mu of the brightness of the reference image xxThe formula is as follows:
wherein x isiThe ith pixel value of the reference image x is represented, N represents the number of pixels, and the brightness mean value mu of the contrast image y can be obtained by the same methody；
With the help of the luminance mean formula of the image, the luminance standard deviation formula of the image x can be obtained:
the brightness standard deviation sigma of the contrast image y can be obtained in the same wayy；
σxyThe structural correlation of the images can be reflected by the covariance of the brightness of the reference image x and the contrast image y, and the formula is:
(3) calculating a brightness comparison function, a contrast comparison function and a structure comparison function:
the brightness comparison function is:
the contrast comparison function is:
the structure degree comparison function is:
wherein, C1、C2、C3Is a small constant set to avoid denominator being zero;
(4) calculating the SSIM value: the formula for SSIM is: SSIM (x, y) ═ l (x, y)]α[c(x,y)]β[s(x,y)]γWherein, α, β and γ are parameters for adjusting the proportion of the 3 comparison functions, and let C be equal to β and γ, and 13＝C2And/2, the above formula can be simplified as follows:
wherein, considering the value range of image brightness and the influence on SSIM value, let C1＝C2The SSIM value indicates the degree of similarity between two images, that is, 0.0001.
8. The wireless microwave rain attenuation model SSIM image similarity evaluation method based on rain cells as claimed in claim 1, wherein when SSIM value is greater than or equal to 0.6, the reference image and the comparison image satisfy the similarity judgment standard.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111340003.6A CN114170427B (en) | 2021-11-12 | 2021-11-12 | Wireless microwave rain attenuation model SSIM image similarity evaluation method based on rain cells |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111340003.6A CN114170427B (en) | 2021-11-12 | 2021-11-12 | Wireless microwave rain attenuation model SSIM image similarity evaluation method based on rain cells |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114170427A true CN114170427A (en) | 2022-03-11 |
| CN114170427B CN114170427B (en) | 2022-09-23 |
Family
ID=80478719
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111340003.6A Active CN114170427B (en) | 2021-11-12 | 2021-11-12 | Wireless microwave rain attenuation model SSIM image similarity evaluation method based on rain cells |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114170427B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116843926A (en) * | 2023-06-15 | 2023-10-03 | 邦邦汽车销售服务(北京)有限公司 | Supply chain tracing method of automobile parts based on data processing and related equipment |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180120418A1 (en) * | 2016-11-02 | 2018-05-03 | National University Of Defense Technology | Method for Regional Attenuation Correction of Radar using a Microwave Links Network |
| WO2018152071A1 (en) * | 2017-02-15 | 2018-08-23 | Flir Systems, Inc. | Systems and methods for efficient enhanced image filtering by collaborative sharpening in similarity domain |
| CN109255100A (en) * | 2018-09-10 | 2019-01-22 | 河海大学 | A kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition |
| JP2019032280A (en) * | 2017-08-09 | 2019-02-28 | 株式会社通信放送国際研究所 | Rain cell model of satellite communication |
| CN110533233A (en) * | 2019-08-20 | 2019-12-03 | 河海大学 | Wireless microwave based on fitness optimization surveys rain link planing method |
| CN112767544A (en) * | 2021-01-06 | 2021-05-07 | 成都理工大学 | Spatial case reasoning method for regional landslide risk evaluation |
| WO2021098083A1 (en) * | 2019-11-22 | 2021-05-27 | 大连理工大学 | Multispectral camera dynamic stereo calibration algorithm based on salient feature |
| WO2021147374A1 (en) * | 2020-01-20 | 2021-07-29 | 北京迈格威科技有限公司 | Image processing method and apparatus, and method and apparatus for training image processing model |
-
2021
- 2021-11-12 CN CN202111340003.6A patent/CN114170427B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180120418A1 (en) * | 2016-11-02 | 2018-05-03 | National University Of Defense Technology | Method for Regional Attenuation Correction of Radar using a Microwave Links Network |
| WO2018152071A1 (en) * | 2017-02-15 | 2018-08-23 | Flir Systems, Inc. | Systems and methods for efficient enhanced image filtering by collaborative sharpening in similarity domain |
| JP2019032280A (en) * | 2017-08-09 | 2019-02-28 | 株式会社通信放送国際研究所 | Rain cell model of satellite communication |
| CN109255100A (en) * | 2018-09-10 | 2019-01-22 | 河海大学 | A kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition |
| CN110533233A (en) * | 2019-08-20 | 2019-12-03 | 河海大学 | Wireless microwave based on fitness optimization surveys rain link planing method |
| WO2021098083A1 (en) * | 2019-11-22 | 2021-05-27 | 大连理工大学 | Multispectral camera dynamic stereo calibration algorithm based on salient feature |
| WO2021147374A1 (en) * | 2020-01-20 | 2021-07-29 | 北京迈格威科技有限公司 | Image processing method and apparatus, and method and apparatus for training image processing model |
| CN112767544A (en) * | 2021-01-06 | 2021-05-07 | 成都理工大学 | Spatial case reasoning method for regional landslide risk evaluation |
Non-Patent Citations (7)
| Title |
|---|
| CHRISTOPHER PURR 等: "Convective rain cell characteristics and scaling in climate projections for Germany", 《RESEARCH ARTICLE》 * |
| LAURENT FÉRAL 等: "HYCELL―A new hybrid model of the rain horizontal distribution for propagation studies: 1. Modeling of the rain cell", 《RADIO SCIENCE》 * |
| 李建勋 等: "基于趋势面与SSIM的时空数据相似度算法", 《计算机工程》 * |
| 李莉: "降雨空间分布模拟及雨衰减分析", 《中国优秀博硕士学位论文全文数据库(硕士) 信息科技辑》 * |
| 林乐科等: "地空路径统计雨衰减建模的几个问题", 《电波科学学报》 * |
| 林裕鹏 等: "基于改进的残差网络的图像去雨方法", 《计算机与数字工程》 * |
| 赵振维 等: "雨衰减预测中的降雨率调整因子概念", 《电波科学学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116843926A (en) * | 2023-06-15 | 2023-10-03 | 邦邦汽车销售服务(北京)有限公司 | Supply chain tracing method of automobile parts based on data processing and related equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114170427B (en) | 2022-09-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110197218B (en) | Thunderstorm strong wind grade prediction classification method based on multi-source convolution neural network | |
| CN110650436B (en) | WiFi data-based position fingerprint database establishing and fitting method | |
| CN106991491B (en) | RBF neural network's environmental pollution monitoring system based on genetic algorithm optimization | |
| CN112182961B (en) | Converter station wireless network channel large-scale fading modeling prediction method | |
| CN110418283B (en) | WiFi data-based location fingerprint database creation method | |
| CN113239971B (en) | Wind field-oriented fusion system for proximity forecast and short-term forecast | |
| CN111610524B (en) | Ice cloud profile inversion method and system based on one-dimensional variational algorithm | |
| CN114170427B (en) | Wireless microwave rain attenuation model SSIM image similarity evaluation method based on rain cells | |
| CN112070103B (en) | Method for inverting atmospheric visibility through microwave link network gridding self-adaptive variable scale | |
| CN113108918B (en) | Method for inverting air temperature by using thermal infrared remote sensing data of polar-orbit meteorological satellite | |
| CN113887908A (en) | Bridge risk assessment method considering subjective and objective cross fusion weight | |
| CN111830070A (en) | Automatic defect identification and judgment system and method based on edge calculation | |
| CN117035673A (en) | Engineering project construction risk management method and management system | |
| CN109657287B (en) | Hydrological model precision identification method based on comprehensive scoring method | |
| CN112801332B (en) | Short-term wind speed prediction method based on gray level co-occurrence matrix | |
| CN116452581B (en) | Intelligent voltage source state detection system and method based on machine vision | |
| CN110913407A (en) | Method and device for analyzing overlapping coverage | |
| CN117635604A (en) | Method for detecting welding quality of constructional engineering steel structure | |
| CN115830302B (en) | Multi-scale feature extraction fusion power distribution network equipment positioning identification method | |
| CN115564180B (en) | Power network reliability assessment method based on big data analysis | |
| CN116739619A (en) | Energy power carbon emission monitoring analysis modeling method and device | |
| CN110781755A (en) | Intelligent identification and metering method and system for construction steel bars | |
| CN114390582B (en) | Base station site prediction method and device | |
| CN113468823B (en) | Optical module damage detection method and system based on machine learning | |
| CN115311238A (en) | Photovoltaic module defect damage identification method based on image analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |